Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Social Network Analysis

What is Twitter

Social Network Analysis: Computer Programs

Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.

Sentiment analysis using deep learning architectures: a review

In these days, there are many news on stock market on the Internet and investors have to understand them immediately to invest in a stock market. In this study we determine sentimental polarities of the stock market news using a polarity dictionary, which consists of terms and their polarities. To achieve our aim we have to construct the polarity dictionary automatically because of decrease of human efforts. In construction the dictionary we use a semi-supervised learning approach. In the semi-supervised approach at first we make a small polarity dictionary, which a word polarity is determined manually, and using many stock market news, which polarities are not known, new words are added in the polarity dictionary. In this paper we proposed an automatically dictionary construction approach and sentiment analysis of stock market news using the dictionary. To discuss our proposed method we compare polarities determined by a financial expert with polarities determined with our proposed method. Hence, we confirm that the proposed method can make an appropriate dictionary.

Sentiment Analysis of Stock Market News with Semi-supervised Learning

In this paper, we propose a news web page classification method (WPCM) The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF) The fixed number of regular words from each class will be used as a feature vectors with the reduced features from the PCA These feature vectors are then used as the input to the neural networks for classification The experimental evaluation demonstrates that the WPCM provides acceptable classification accuracy with the sports news datasets

Web news classification using neural networks based on PCA

It is important to classify documents according to their contents because of finding necessary documents efficiently. To achieve good classification document similarity estimation is one of key techniques since classification is executed based on the document similarity. In natural language processing bag-of-words model is used to extract features from documents and term occurrence frequency based value is used as a weight of each features. However, the term weight methodologies usually use predefined models and include some limitations. New approaches to construct feature vectors based on data distribution are desired to achieve high performance of natural language processing. These days many researchers pay attention to deep learning. Deep learning is a new approach to transform raw data to feature vectors using many unlabeled data. This characteristics is desirable to satisfy a previous need. In natural language processing a main aim is to construct a language model on a deep architecture neural network. In this paper we use a deep architecture neural network to estimate document similarity. To obtain good article similarity estimation we have to generate good article vectors that can represent all article characteristics. Hence, we use many stock market news to train the deep architecture neural network and generate article vectors with the trained neural network. And we calculate cosine similarity between labeled articles and discuss performance of the deep architecture neural network. In evaluation we do not focus on articles' contents but on their sentiment polarity. Hence, we discuss whether the proposed method classifies articles according to their sentiment polarity or not. We confirmed though the proposed method is an unsupervised learning approach, it achieves good performance in stock market news similarity estimation. The results show a deep architecture neural network can be applied to more natural language processing tasks.

Document similarity estimation for sentiment analysis using neural network

Automatic headline generation is related to automatic text summarization and it is useful to solve information flood problems. This paper aims at generating a headline using a recurrent neural network which is based on a machine translation approach. Our headline generator consists of an encoder and a decoder and they are constructed with Long Short Term Memory, which is one of recurrent neural networks. The encoder constructs distributed representation from the first sentence in an article and the decoder generated headlines from the distributed representation. In our experiments, we confirmed that our proposed method could generate appropriate headlines but in some articles this method generates meaningless headlines. The results show that our proposed method is superior to another approach, statistical machine translation from the viewpoint of ROUGE, which is an evaluation score of automatic text summarization. Furthermore, we could find that using an input sentence in reverse order improves the quality of headline generation.

Headline Generation with Recurrent Neural Network

We propose a relationship strength estimation method in social media. We estimate relationship strength between web pages in social bookmarking services using a tag vocabulary and construct a network of the web pages. In this step Bayes theorem is used to estimate true strength from each user's strength estimation. After estimation using tags the network is represented in lower dimension space and some non-important links are removed. In this step the network is approximated keeping neighborhood of data in the original network. To evaluate our proposed method we carry out some experiments using artificially generated data and real social bookmarking data. And we confirm that 1) the proposed method can estimate more appropriate relationship strength than ordinary methods based on cooccurrence frequency and tags sharing rate and 2) the proposed method can remain essential links and delete pseudo relationship.

Hidekazu Yanagimoto

Papers

Sentiment Analysis of Stock Market News with Semi-supervised Learning

Web news classification using neural networks based on PCA

Document similarity estimation for sentiment analysis using neural network

Headline Generation with Recurrent Neural Network

Relationship strength estimation for social media using Folksonomy and network analysis